We take a multi-view learning approach to combine both stylometric and personality features into a comprehensive embedding for each user. We use Canonical Correlation Analysis (CCA) [1]} to perform this fusion. CCA captures maximal information between two views and creates a combined representation [2]}, [3]}. In the event of having more than two views, fusion can be performed using an extension of CCA called Generalized CCA (see Supplementary).
